The Experts below are selected from a list of 33786 Experts worldwide ranked by ideXlab platform
Jerry L. Prince - One of the best experts on this subject based on the ideXlab platform.
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an overview of the multi object geometric Deformable Model approach in biomedical imaging
2016Co-Authors: Aaron Carass, Jerry L. PrinceAbstract:Abstract Deformable Models have been extensively used for various image segmentation tasks and to a lesser extent in image registration. They have been principally concerned with the identification of a single object surrounded by background. There have been numerous proposals to adapt Deformable Models for segmenting multiple objects, though often with limitations. In this work, we present a description of multi-object geometric Deformable Model (MGDM) approach in an N-dimensional space. The MGDM framework has unique features including no gaps between objects, no overlaps, and the topology of individual objects and any groups of bordering objects can be preserved. MGDM defines objects in terms of their boundaries and as such enables boundary-specific speeds. The framework can maintain M objects with 2N functions in an N-dimensional space, providing considerable computational efficiency. The multi-object framework provides a critical capability in the parcellation of objects in a variety of situations. We include an illustrative example of multiple-object topology preservation on a human hand and also examples of MGDM for cell segmentation, retinal layer segmentation in optical coherence tomography, and cerebellar lobule segmentation from magnetic resonance imaging.
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multiple object geometric Deformable Model for segmentation of macular oct
2014Co-Authors: Aaron Carass, Andrew Lang, Matthew Hauser, Peter A Calabresi, Howard S Ying, Jerry L. PrinceAbstract:Optical coherence tomography (OCT) is the de facto standard imaging modality for ophthalmological assessment of retinal eye disease, and is of increasing importance in the study of neurological disorders. Quantification of the thicknesses of various retinal layers within the macular cube provides unique diagnostic insights for many diseases, but the capability for automatic segmentation and quantification remains quite limited. While manual segmentation has been used for many scientific studies, it is extremely time consuming and is subject to intra- and inter-rater variation. This paper presents a new computational domain, referred to as flat space, and a segmentation method for specific retinal layers in the macular cube using a recently developed Deformable Model approach for multiple objects. The framework maintains object relationships and topology while preventing overlaps and gaps. The algorithm segments eight retinal layers over the whole macular cube, where each boundary is defined with subvoxel precision. Evaluation of the method on single-eye OCT scans from 37 subjects, each with manual ground truth, shows improvement over a state-of-the-art method.
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segmentation of the complete superior cerebellar peduncles using a multi object geometric Deformable Model
2013Co-Authors: Chuyang Ye, John A Bogovic, Sarah H Ying, Jerry L. PrinceAbstract:The superior cerebellar peduncles (SCPs) are white matter tracts that serve as the major efferent pathways from the cerebellum to the thalamus. With diffusion tensor images (DTI), tractography algorithms or volumetric segmentation methods have been able to reconstruct part of the SCPs. However, when the fibers cross, the primary eigenvector (PEV) no longer represents the primary diffusion direction. Therefore, at the crossing of the left and right SCP, known as the decussation of the SCPs (dSCP), fiber tracts propagate incorrectly. To our knowledge, previous methods have not been able to segment the SCPs correctly. In this work, we explore the diffusion properties and seek to volumetrically segment the complete SCPs. The non-crossing SCPs and dSCP are Modeled as different objects. A multi-object geometric Deformable Model is employed to define the boundaries of each piece of the SCPs, with the forces derived from diffusion properties as well as the PEV. We tested our method on a software phantom and real subjects. Results indicate that our method is able to the resolve the crossing and segment the complete SCPs with repeatability.
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automatic cell segmentation in fluorescence images of confluent cell monolayers using multi object geometric Deformable Model
2013Co-Authors: Zhen Yang, Aaron Carass, John A Bogovic, Mao Ye, Peter C Searson, Jerry L. PrinceAbstract:With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric Deformable Model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.
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parcellation of the thalamus using diffusion tensor images and a multi object geometric Deformable Model
2013Co-Authors: Chuyang Ye, John A Bogovic, Sarah H Ying, Jerry L. PrinceAbstract:The thalamus is a sub-cortical gray matter structure that relays signals between the cerebral cortex and midbrain. It can be parcellated into the thalamic nuclei which project to different cortical regions. The ability to automatically parcellate the thalamic nuclei could lead to enhanced diagnosis or prognosis in patients with some brain disease. Previous works have used diffusion tensor images (DTI) to parcellate the thalamus, using either tensor similarity or cortical connectivity as information driving the parcellation. In this paper, we propose a method that uses the diffusion tensors in a different way than previous works to guide a multiple object geometric Deformable Model (MGDM) for parcellation. The primary eigenvector (PEV) is used to indicate the homogeneity of fiber orientations. To remove the ambiguity due to the fact that the PEV is an orientation, we map the PEV into a 5D space known as the Knutsson space. An edge map is then generated from the 5D vector to show divisions between regions of aligned PEV’s. The generalized gradient vector flow (GGVF) calculated from the edge map drives the evolution of the boundary of each nucleus. Region based force, balloon force, and curvature force are also employed to refine the boundaries. Experiments have been carried out on five real subjects. Quantitative measures show that the automated parcellation agrees with the manual delineation of an expert under a published protocol.
Aaron Carass - One of the best experts on this subject based on the ideXlab platform.
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an overview of the multi object geometric Deformable Model approach in biomedical imaging
2016Co-Authors: Aaron Carass, Jerry L. PrinceAbstract:Abstract Deformable Models have been extensively used for various image segmentation tasks and to a lesser extent in image registration. They have been principally concerned with the identification of a single object surrounded by background. There have been numerous proposals to adapt Deformable Models for segmenting multiple objects, though often with limitations. In this work, we present a description of multi-object geometric Deformable Model (MGDM) approach in an N-dimensional space. The MGDM framework has unique features including no gaps between objects, no overlaps, and the topology of individual objects and any groups of bordering objects can be preserved. MGDM defines objects in terms of their boundaries and as such enables boundary-specific speeds. The framework can maintain M objects with 2N functions in an N-dimensional space, providing considerable computational efficiency. The multi-object framework provides a critical capability in the parcellation of objects in a variety of situations. We include an illustrative example of multiple-object topology preservation on a human hand and also examples of MGDM for cell segmentation, retinal layer segmentation in optical coherence tomography, and cerebellar lobule segmentation from magnetic resonance imaging.
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multiple object geometric Deformable Model for segmentation of macular oct
2014Co-Authors: Aaron Carass, Andrew Lang, Matthew Hauser, Peter A Calabresi, Howard S Ying, Jerry L. PrinceAbstract:Optical coherence tomography (OCT) is the de facto standard imaging modality for ophthalmological assessment of retinal eye disease, and is of increasing importance in the study of neurological disorders. Quantification of the thicknesses of various retinal layers within the macular cube provides unique diagnostic insights for many diseases, but the capability for automatic segmentation and quantification remains quite limited. While manual segmentation has been used for many scientific studies, it is extremely time consuming and is subject to intra- and inter-rater variation. This paper presents a new computational domain, referred to as flat space, and a segmentation method for specific retinal layers in the macular cube using a recently developed Deformable Model approach for multiple objects. The framework maintains object relationships and topology while preventing overlaps and gaps. The algorithm segments eight retinal layers over the whole macular cube, where each boundary is defined with subvoxel precision. Evaluation of the method on single-eye OCT scans from 37 subjects, each with manual ground truth, shows improvement over a state-of-the-art method.
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automatic cell segmentation in fluorescence images of confluent cell monolayers using multi object geometric Deformable Model
2013Co-Authors: Zhen Yang, Aaron Carass, John A Bogovic, Mao Ye, Peter C Searson, Jerry L. PrinceAbstract:With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric Deformable Model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.
Isabelle Bloch - One of the best experts on this subject based on the ideXlab platform.
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3d brain tumor segmentation in mri using fuzzy classification symmetry analysis and spatially constrained Deformable Models
2009Co-Authors: Hassan Khotanlou, Olivier Colliot, Jamal Atif, Isabelle BlochAbstract:We propose a new general method for segmenting brain tumors in 3D magnetic resonance images. Our method is applicable to different types of tumors. First, the brain is segmented using a new approach, robust to the presence of tumors. Then a first tumor detection is performed, based on selecting asymmetric areas with respect to the approximate brain symmetry plane and fuzzy classification. Its result constitutes the initialization of a segmentation method based on a combination of a Deformable Model and spatial relations, leading to a precise segmentation of the tumors. Imprecision and variability are taken into account at all levels, using appropriate fuzzy Models. The results obtained on different types of tumors have been evaluated by comparison with manual segmentations.
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integration of fuzzy spatial relations in Deformable Models application to brain mri segmentation
2006Co-Authors: Olivier Colliot, Oscar Camara, Isabelle BlochAbstract:This paper presents a general framework to integrate a new type of constraints, based on spatial relations, in Deformable Models. In the proposed approach, spatial relations are represented as fuzzy subsets of the image space and incorporated in the Deformable Model as a new external force. Three methods to construct an external force from a fuzzy set representing a spatial relation are introduced and discussed. This framework is then used to segment brain subcortical structures in magnetic resonance images (MRI). A training step is proposed to estimate the main parameters defining the relations. The results demonstrate that the introduction of spatial relations in a Deformable Model can substantially improve the segmentation of structures with low contrast and ill-defined boundaries.
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description of brain internal structures by means of spatial relations for mr image segmentation
2004Co-Authors: Olivier Colliot, Oscar Camara, Remi Dewynter, Isabelle BlochAbstract:This paper presents a method for segmenting internal brain structures in MR images. It introduces prior information in an original way through descriptions of the spatial arrangement of structures by means of spatial relations, which are represented in the fuzzy set framework. The method is hierarchical as the segmentation of a given structure is based on the previously segmented ones. The processing of each structure is decomposed into two stages: an initialization stage which makes extensive use of prior knowledge and a refinement stage using a 3D Deformable Model. The Deformable Model is guided by an external force representing the combination of a classical data term derived from an edge map and a force corresponding to a given spatial relation. We propose different ways to compute a force from a fuzzy set representing a relation or a combination of relations. Results obtained for the lateral ventricles, the third ventricle, the caudate nuclei and the thalami are promising. The proposed combination of spatial relations and Deformable Models has proved to be very useful to segment parts of the structures were no visible edges are present, improving the segmentation accuracy.
Vladimir Pekar - One of the best experts on this subject based on the ideXlab platform.
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automated segmentation of the left ventricle in cardiac mri
2004Co-Authors: Michael Kaus, Jurgen Weese, Jens Von Berg, Wiro J Niessen, Vladimir PekarAbstract:We present a fully automated Deformable Model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a Deformable Model. Inter-individual shape variation is represented by a statistical point distribution Model, and the spatial relationship of the epi- and endocardium is Modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature Model is established by classification of grey value surface profiles. Quantitative validation of 121 3D MRI datasets in end-diastolic (end-systolic) phase demonstrates accuracy and robustness, with 2.45 mm (2.84 mm) mean deviation from manual segmentation.
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shape constrained Deformable Models for 3d medical image segmentation
2001Co-Authors: Jurgen Weese, Michael Kaus, Christian Lorenz, Steven Lobregt, Roel Truyen, Vladimir PekarAbstract:To improve the robustness of segmentation methods, more and more methods use prior knowledge. We present an approach which embeds an active shape Model into an elastically Deformable surface Model, and combines the advantages of both approaches. The shape Model constrains the flexibility of the surface mesh representing the Deformable Model and maintains an optimal distribution of mesh vertices. A specific external energy which attracts the Deformable Model to locally detected surfaces, reduces the danger that the mesh is trapped by false object boundaries. Examples are shown, and furthermore a validation study for the segmentation of vertebrae in CT images is presented. With the exception of a few problematic areas, the algorithm leads reliably to a very good overall segmentation.
Theoharis Theoharis - One of the best experts on this subject based on the ideXlab platform.
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partial matching of interpose 3d facial data for face recognition
2009Co-Authors: Panagiotis Perakis, George Toderici, Georgios Passalis, Theoharis Theoharis, Ioannis A. KakadiarisAbstract:Three-dimensional face recognition has lately received much attention due to its robustness in the presence of lighting and pose variations. However, certain pose variations often result in missing facial data. This is common in realistic scenarios, such as uncontrolled environments and uncooperative subjects. Most previous 3D face recognition methods do not handle extensive missing data as they rely on frontal scans. Currently, there is no method to perform recognition across scans of different poses. A unified method that addresses the partial matching problem is proposed. Both frontal and side (left or right) facial scans are handled in a way that allows interpose retrieval operations. The main contributions of this paper include a novel 3D landmark detector and a Deformable Model framework that supports symmetric fitting. The landmark detector is utilized to detect the pose of the facial scan. This information is used to mark areas of missing data and to roughly register the facial scan with an Annotated Face Model (AFM). The AFM is fitted using a Deformable Model framework that introduces the method of exploiting facial symmetry where data are missing. Subsequently, a geometry image is extracted from the fitted AFM that is independent of the original pose of the facial scan. Retrieval operations, such as face identification, are then performed on a wavelet domain representation of the geometry image. Thorough testing was performed by combining the largest publicly available databases. To the best of our knowledge, this is the first method that handles side scans with extensive missing data (e.g., up to half of the face missing).
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three dimensional face recognition in the presence of facial expressions an annotated Deformable Model approach
2007Co-Authors: Ioannis A. Kakadiaris, George Toderici, Georgios Passalis, M N Murtuza, Yunliang Lu, Nikos Karampatziakis, Theoharis TheoharisAbstract:In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a Deformable Model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality
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evaluation of 3d face recognition in the presence of facial expressions an annotated Deformable Model approach
2005Co-Authors: Georgios Passalis, George Toderici, Ioannis A. Kakadiaris, Theoharis Theoharis, N MurtuzaAbstract:From a users perspective, face recognition is one of the most desirable biometrics, due to its non-intrusive nature; however, variables such as face expression tend to severely affect recognition rates. We have applied to this problem our previous work on elastically adaptive Deformable Models to obtain parametric representations of the geometry of selected localized face areas using an annotated face Model. We then use wavelet analysis to extract a compact biometric signature, thus allowing us to perform rapid comparisons on either a global or a per area basis. To evaluate the performance of our algorithm, we have conducted experiments using data from the Face Recognition Grand Challenge data corpus, the largest and most established data corpus for face recognition currently available. Our results indicate that our algorithm exhibits high levels of accuracy and robustness, and is not gender biased. In addition, it is minimally affected by facial expressions.